from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceTB/SmolLM2-135M",
    device_map="auto",
    torch_dtype="auto"
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "CrystalRaindropsFall/smollm2-gsm8k-curriculum-complexity")

# Inference
prompt = "Question: Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?\nAnswer:"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Using with Pipeline

python

from transformers import pipeline
from peft import PeftModel, AutoPeftModelForCausalLM

# Load model with adapter
model = AutoPeftModelForCausalLM.from_pretrained(
    "YOUR_USERNAME/REPO_NAME",
    device_map="auto"
)

# Create pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Generate
result = pipe("Question: A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?\nAnswer:")
print(result[0]['generated_text'])

Performance

Evaluated on GSM8K test set (512 samples):

Metric	Score
Exact Match	2.93%
Format Correct	100%

Limitations

Trained on grade school level math problems
May struggle with problems requiring external knowledge
Performance depends on problem complexity and wording
Best used with base model's standard generation settings

Acknowledgments

Base model: HuggingFaceTB/SmolLM2-135M
Dataset: GSM8K by Cobbe et al.
Training framework: HuggingFace PEFT

License

Apache 2.0 (following base model license)